adding typos

doc/pub/How2ReadData/html/How2ReadData.html

@@ -140,7 +140,7 @@ MathJax.Hub.Config({
 <center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
 <br>
 <p>
-<center><h4>Dec 22, 2019</h4></center> <!-- date -->
+<center><h4>Dec 24, 2019</h4></center> <!-- date -->
 <br>
 
 <h2 id="___sec0">Introduction </h2>
@@ -1170,10 +1170,10 @@ Finally, another cost function is the Huber cost function used in robust regress
 It is less sensitive to outliers in data than the squared error cost function.
 A variant for classification is also sometimes used, a quantity we will meet later. 
 $$
-L_{\delta }(a)={\begin{cases}{\frac {1}{2}}{a^{2}}&{\text{for }}|a|\leq \delta ,\\\delta (|a|-{\frac {1}{2}}\delta ),&{\text{otherwise.}}\end{cases}}}.
+H_{\delta}(a)={\begin{cases}{\frac {1}{2}}{a^{2}}&{\text{for }}|a|\leq \delta ,\\\delta (|a|-{\frac {1}{2}}\delta ),&{\text{otherwise.}}\end{cases}}}.
 $$
 
-<p>
+Here \( a=\boldsymbol{y} - \boldsymbol{\tilde{y}} \).
 We will discuss in more
 detail these and other functions in the various lectures.  We conclude this part with another example. Instead of 
 a linear \( x \)-dependence we study now a cubic polynomial and use the polynomial regression analysis tools of scikit-learn.